agentstax-eval

agentstax-eval

A lightweight Python library for evaluating AI agents and RAG pipelines. No magic, no vendor lock-in — just explicit, readable evaluation logic.

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Table of Contents

• Key Features
• Installation
• Why agentstax-eval
• Quickstart
• Metrics
• Providers
• Monitoring Dashboard
• Core API
• Framework Auto-Extraction
• Caching
• Multi-Agent Evaluation
• Async Support
• CI / Regression Testing
• Further Reading
• Contributing
• License

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Key Features

• Four objects, one job each — Dataset, Task, Evaluation, Pipeline.
• Zero required dependencies — the core library installs with no third-party packages.
• Bring your own LLM — pass any fn(prompt: str) -> str as a judge. No default provider.
• Metrics are functions — fn(dataset_row: dict) -> float. No base classes, no decorators.
• Built-in LLM-as-judge — correctness, relevance, faithfulness, completeness, rubric.
• Framework auto-extraction — pass a LangGraph, Google ADK, OpenAI Agents, CrewAI, LlamaIndex, or MSAF agent to Pipeline and get topology, model, tools, and system prompt extracted automatically.
• Agent fingerprinting — topology changes are hashed, so caches auto-invalidate and the monitoring dashboard detects architecture drift.
• LLM response caching — DiskCache and MemoryCache keyed on judge + prompt + agent fingerprint.
• Real-time monitoring — pair with agentstax-eval-monitor for a live dashboard with regression detection, metric trends, and agent network visualization.
• CI-ready — assert_passing(), failures(), and JSON save/load for pytest regression tests.

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Installation

pip install agentstax-eval

Requires: Python 3.9+. Zero core dependencies.

Optional extras for built-in provider functions:

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Why agentstax-eval

All the eval frameworks I have used feel more complicated than they should. They are either too heavy, too complicated, or require vendor lock-in.

I often find myself creating a simple script to test what I want, which works great in the beginning but shows its weakness later on.

I built agentstax-eval to fix that. It's simple when you need it, but flexible enough to grow with your agent architecture.

It works with whatever you're already using — you can pass your LangGraph, ADK, OpenAI Agents, CrewAI, LlamaIndex, or MSAF agent directly to Pipeline. It will walk the hierarchy, extract the topology, and fingerprint it. When your architecture changes, the cache invalidates and the monitor flags the drift.

Pair it with agentstax-eval-monitor and you get a live dashboard that shows regression detection, metric trends, and agent network visualization.

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Quickstart

from openai import OpenAI
from agentstax_eval import Pipeline, Dataset, Task, Evaluation
from agentstax_eval.metrics import llm_correctness
from agentstax_eval.providers import openai_provider

client = OpenAI()

dataset = Dataset([
    {"question": "What is the capital of France?", "expected_answer": "Paris"},
    {"question": "Who wrote Hamlet?",              "expected_answer": "Shakespeare"},
    {"question": "What is 2 + 2?",                "expected_answer": "4"},
])

def get_answer(dataset_row: dict) -> dict:
    response = client.chat.completions.create(
        model="gpt-4o-mini",
        messages=[{"role": "user", "content": dataset_row["question"]}],
    )
    return {"answer": response.choices[0].message.content}

pipeline = Pipeline(
    dataset=dataset,
    tasks=[Task(get_answer)],
    evaluation=Evaluation(metrics=[llm_correctness(llm=openai_provider())]),
)

results = pipeline.run()
results.save(directory="results", base_filename="eval")
print(results)

With agent auto-extraction

Pass your agent object directly to unlock automatic metadata extraction, topology mapping, and smart caching:

from agentstax_eval import Pipeline, Dataset, Task, Evaluation, DiskCache
from agentstax_eval.metrics import llm_correctness
from agentstax_eval.providers import openai_provider

pipeline = Pipeline(
    dataset=dataset,
    tasks=[Task(get_answer)],
    evaluation=Evaluation(metrics=[llm_correctness(llm=openai_provider())]),
    agent=my_langgraph_agent,           # auto-extracts topology, model, tools
    cache=DiskCache(".cache"),           # caches judge responses, invalidates on topology change
)

results = pipeline.run()
results.save(directory="results", base_filename="my_agent_eval")

The saved JSON now includes a topology field with the full agent graph, fingerprint, and per-node metadata — which the monitoring dashboard uses for network visualization and change detection.

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Metrics

Deterministic

from agentstax_eval.metrics import exact_match, contains_answer, json_valid

LLM-as-Judge

Factory functions that take an llm callable and return a metric. The llm can be synchronous (fn(prompt: str) -> str) or asynchronous (async def).

from agentstax_eval.metrics import llm_correctness, llm_relevance, llm_faithfulness, llm_completeness, llm_rubric

All use a six-point scale (1.0, 0.8, 0.6, 0.4, 0.2, 0.0) with chain-of-thought reasoning.

Custom Metrics

Any function with the signature fn(dataset_row: dict) -> float works as a metric. The function receives the full row dict — including question, expected_answer, answer, and any extra fields your tasks added (like context). Return a float between 0.0 and 1.0. The function's __name__ is used as the column name in results.

# Deterministic metric — no LLM needed
def answer_is_concise(dataset_row: dict) -> float:
    return 1.0 if len(dataset_row["answer"].split()) <= 20 else 0.0

# Custom LLM metric — you control the prompt and parsing
def my_judge_metric(dataset_row: dict) -> float:
    prompt = f"Is this answer polite? Answer 1 or 0.\nAnswer: {dataset_row['answer']}"
    response = client.chat.completions.create(
        model="gpt-4o",
        messages=[{"role": "user", "content": prompt}],
    )
    return float(response.choices[0].message.content.strip())

evaluation = Evaluation(metrics=[exact_match, answer_is_concise, my_judge_metric])

When using factory functions or lambdas, set __name__ to control the result column name:

concise = llm_rubric(llm=judge, criteria="Two sentences or fewer.")
concise.__name__ = "rubric_concise"

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Providers

Built-in fn(prompt: str) -> str wrappers that handle client setup and set judge_model metadata automatically:

from agentstax_eval.providers import openai_provider, anthropic_provider, google_provider

judge = openai_provider()                            # default: gpt-4.1, reads OPENAI_API_KEY
judge = anthropic_provider(model="claude-opus-4-6")  # reads ANTHROPIC_API_KEY
judge = google_provider(model="gemini-2.5-pro")      # reads GEMINI_API_KEY

Custom Providers

A provider is any fn(prompt: str) -> str. To get automatic judge_model tracking in result metadata, set the attribute on the function:

from openai import OpenAI

client = OpenAI()

def my_judge(prompt: str) -> str:
    response = client.chat.completions.create(
        model="gpt-4o",
        messages=[{"role": "user", "content": prompt}],
    )
    return response.choices[0].message.content

my_judge.judge_model = "openai/gpt-4o"

The judge_model string flows into metadata.scoring.<metric_name>.judge_model in saved results. If omitted, scoring metadata will simply not include it.

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Monitoring Dashboard

agentstax-eval-monitor is a companion real-time dashboard that watches a directory of agentstax-eval result files and provides:

• Regression detection — agents categorized as regressed, improved, or healthy based on metric deltas
• Metric trend sparklines — per-metric performance history at a glance
• Per-agent deep dives — zoomable line charts with threshold lines and metadata change markers
• Agent network visualization — interactive DAG of your multi-agent topology, with added/removed agents highlighted
• Architecture drift detection — visual timeline of when topology or metric config changed
• Live updates — WebSocket-powered, updates as new result files land

Setup

# Install
cd agentstax-eval-monitor
bun install

# Point at your results directory
bun run index.ts ./results

Opens a dashboard at http://localhost:3000.

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Core API

Dataset

Wraps a list of dicts. Each row must have a question. The expected_answer field is optional at the dataset level — metrics that need it raise MissingFieldError at scoring time.

dataset = Dataset([
    {"question": "What year did WWII end?", "expected_answer": "1945"},
])

Task

Wraps a callable fn(dataset_row: dict) -> dict. The returned dict is merged into each row.

task = Task(get_answer)
answered_dataset = task.run(dataset)  # returns new Dataset, original not mutated

Evaluation

Holds a list of metrics and scores a dataset. Metrics are fn(dataset_row: dict) -> float.

evaluation = Evaluation(metrics=[exact_match, llm_correctness(llm=my_judge)])
results = evaluation.run(answered_dataset, metadata={"model": "gpt-4o"})

Pipeline

Convenience wrapper: runs tasks sequentially, then scores. Accepts agent for auto-extraction and cache for LLM judge caching.

pipeline = Pipeline(
    dataset=dataset,
    tasks=[Task(get_answer)],
    evaluation=Evaluation(metrics=[llm_correctness(llm=my_judge)]),
    agent=my_agent,                # optional: auto-extract topology
    cache=DiskCache(".cache"),     # optional: cache judge responses (requires agent)
    metadata={"experiment": "v2"}, # optional: merged into results (overrides extracted values)
)
results = pipeline.run()

EvaluationResults

results.metadata         # dict with timestamp_utc, topology, scoring, etc.
results.rows             # list[ResultRow] — each has .data (dict) and .scores (dict)
results.assert_passing() # True if all thresholded metrics pass on every row
results.failures()       # list[Failure] — each row/metric pair below threshold

# Save and load
path = results.save(directory="results", base_filename="eval")
latest = EvaluationResults.load(directory="results", base_filename="eval")

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Framework Auto-Extraction

Pass an agent object from a supported framework and Pipeline automatically extracts its metadata and topology — no manual metadata dict needed.

Supported frameworks:

pipeline = Pipeline(
    dataset=dataset,
    tasks=[Task(get_answer)],
    evaluation=Evaluation(metrics=[llm_correctness(llm=judge)]),
    agent=my_agent,  # just pass your agent
)

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Caching

Cache LLM judge responses to avoid redundant API calls. Cache keys include judge model, prompt, and agent fingerprint — the cache auto-invalidates when agent topology changes.

from agentstax_eval import DiskCache, MemoryCache

# Persistent (JSON file, atomic writes, thread-safe)
cache = DiskCache(directory=".cache")

# In-process (lost when process exits, thread-safe)
cache = MemoryCache()

Pass to Pipeline with agent for automatic fingerprint extraction:

pipeline = Pipeline(
    dataset=dataset,
    tasks=[Task(get_answer)],
    evaluation=Evaluation(metrics=[llm_correctness(llm=judge)]),
    agent=my_agent,
    cache=DiskCache(".cache"),
)

results = pipeline.run()
cache.stats()  # {"hits": 0, "misses": 3, "size": 3}

# Second run with same agent topology: all hits
results = pipeline.run()
cache.stats()  # {"hits": 3, "misses": 3, "size": 3}

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Async Support

All three core objects support run_async(concurrency=N) for concurrent row processing:

import asyncio
from agentstax_eval import Pipeline, Task, Evaluation
from agentstax_eval.metrics import exact_match

async def call_agent(dataset_row: dict) -> dict:
    response = await async_client.chat.completions.create(
        model="gpt-4o-mini",
        messages=[{"role": "user", "content": dataset_row["question"]}],
    )
    return {"answer": response.choices[0].message.content}

pipeline = Pipeline(
    dataset=dataset,
    tasks=[Task(call_agent)],
    evaluation=Evaluation(metrics=[exact_match]),
    agent=my_agent,
)

results = asyncio.run(pipeline.run_async(concurrency=5))

Task.run_async() and Evaluation.run_async() are also available for step-by-step use. Default concurrency is 10.

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CI / Regression Testing

# test_agent_quality.py
from agentstax_eval import Pipeline, Dataset, Task, Evaluation
from agentstax_eval.metrics import llm_correctness
from agentstax_eval.providers import openai_provider

def test_agent_passes_threshold():
    results = Pipeline(
        dataset=Dataset([
            {"question": "Capital of France?", "expected_answer": "Paris"},
            {"question": "What is 2 + 2?", "expected_answer": "4"},
        ]),
        tasks=[Task(call_my_agent)],
        evaluation=Evaluation(metrics=[llm_correctness(llm=openai_provider())]),
        agent=my_agent,
    ).run()

    assert results.assert_passing(), "\n".join(str(f) for f in results.failures())

Save results for trend tracking — the monitoring dashboard can watch the same directory for live regression visibility.

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Further Reading

• LLM Judge Prompt Writing Guide — rubric design, scoring scales, bias mitigation, and prompt examples.

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Contributing

Contributions are welcome. Please open an issue to discuss before submitting a PR.

git clone https://github.com/your-org/agentstax-eval.git && cd agentstax-eval
uv sync && uv run pytest --ignore=tests/e2e -q

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License

MIT — see LICENSE for details.